The ability to control data and information through the Internet can be challenging. Preliminary analysis showed that some tampering and forgery may occur to some words of the Quran in the electronic versions that spa...
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In the current era of technology, the use of IoT devices have become increasingly popular. On the contrary, there is a growing concern of security in these resource constrained devices as they don't have the compu...
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In recent years, the rapid development of remote sensing (RS) technology has led to a drastic increase in the availability of RS images. This calls for the need to develop new methods able to effectively and efficient...
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In recent years, the rapid development of remote sensing (RS) technology has led to a drastic increase in the availability of RS images. This calls for the need to develop new methods able to effectively and efficiently retrieve the required instances from a massive amount of RS imagery. In retrieval tasks, finding the nearest-neighbor sample of the retrieval query is a fundamental research topic. Exhaustive comparison is the simplest method to accomplish this task. However, due to the involved computational complexity and memory limitations, this solution is no longer feasible in large data retrieval tasks. As an important branch of approximate nearest-neighbor retrieval (NNR), hash algorithms transform high-dimensional data into low-bit expressions (hash codes) with elements of 0 and 1 to reduce storage and computational costs. Hash algorithms aim to preserve the same nearest-neighbor relationship between the learned hash codes and the original data. Existing hash algorithms are divided into two classes: shallow and deep methods. Furthermore, deep hash algorithms can be divided into (semi-) supervised and unsupervised algorithms. In this article, representative hash-based RS image retrieval (HBRSIR) methods are reviewed, studying the application of hashing in other areas of the RS community and introducing available datasets and evaluation metrics for RS image retrieval (RSIR). The performance of representative and cross-modal hashing methods is validated using two common RSIR datasets (UCMerced and AID) and a cross-modal dataset (DSRSID). Prospects of future work summarizing HBRSIR are also provided.
Wireless Sensor Networks (WSN) are particularly special due to many characteristics, such as a working environment that makes maintenance and support a challenge; and hardware resources, particularly memory, processin...
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Wireless Sensor Networks (WSN) are particularly special due to many characteristics, such as a working environment that makes maintenance and support a challenge; and hardware resources, particularly memory, processing and battery power, that make it capable of handling only limited software. Consequently, the administration of WSN is becoming a challenge. To overcome these limitations we proposed Distributed Policy-Based Management (DBPM) framework. Our proposed framework is expected to conceal the complexity of administrating policies operations from the users by simplifying the deployment processes. Bloom Filter is an elegant data structure that answers the membership inquiry with no false negative and manageable false positive. In this paper we propose utilizing Bloom Filter in Distributed Policy-Based Management (DPBM) environment in WSN to confirm the existence of any policy within the WSN, which will reduce the traffic within the network as well as preserve the sensor node energy.
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